NmerA, the Metal Binding Domain of Mercuric Ion Reductase, Removes Hg<sup>2+</sup> from Proteins, Delivers It to the Catalytic Core, and Protects Cells under Glutathione-Depleted Conditions<sup>,</sup>
Bibliographic record
Abstract
The ligand binding and catalytic properties of heavy metal ions have led to the evolution of metal ion-specific pathways for control of their intracellular trafficking and/or elimination. Small MW proteins/domains containing a GMTCXXC metal binding motif in a betaalphabetabetaalphabeta fold are common among proteins controlling the mobility of soft metal ions such as Cu(1+), Zn(2+), and Hg(2+), and the functions of several have been established. In bacterial mercuric ion reductases (MerA), which catalyze reduction of Hg(2+) to Hg(0) as a means of detoxification, one or two repeats of sequences with this fold are highly conserved as N-terminal domains (NmerA) of uncertain function. To simplify functional analysis of NmerA, we cloned and expressed the domain and catalytic core of Tn501 MerA as separate proteins. In this paper, we show Tn501 NmerA to be a stable, soluble protein that binds 1 Hg(2+)/domain and delivers it to the catalytic core at kinetically competent rates. Comparison of steady-state data for full-length versus catalytic core MerA using Hg(glutathione)(2) or Hg(thioredoxin) as substrate demonstrates that the NmerA domain does participate in acquisition and delivery of Hg(2+) to the catalytic core during the reduction catalyzed by full-length MerA, particularly when Hg(2+) is bound to a protein. Finally, comparison of growth curves for glutathione-depleted Escherichia coli expressing either catalytic core, full-length, or a combination of core plus NmerA shows an increased protection of cells against Hg(2+) in the media when NmerA is present, providing the first evidence of a functional role for this highly conserved domain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".